Automated Brain Lesion Classification Using Hybrid Fuzzy C-Means With Correlation Template and Wavelet Transform

Abstract

This paper presents a new technique for automatically detecting and characterizing major brain lesions for diffusion-weighted imaging. The analytical framework consists of pre-processing, segmentation, features extraction and classification. For segmentation process, Fuzzy C-Means integrated with correlation template are proposed to detect the lesion region. The algorithm performance is evaluated using Jaccard and both false positive and false negative rates. Next, the features from wavelet transform are extracted from the region and fed into the rule-based classifier. Results demonstrated that FCM with correlation template offered the best performance for acute stroke segmentation with the highest rate of 0.77 Jaccard index. The classification accuracy for acute stroke, tumor, chronic stroke and necrosis are 94%, 97, 63% and 60%. In conclusion, the proposed hybrid analysis has the potential to be explored as a computer-aided tool to detect and diagnose of human brain lesion.